avinashyaganapu / puzzlecam

Puzzle-CAM: Improved localization via matching partial and full features.

Puzzle-CAM

The official implementation of "Puzzle-CAM: Improved localization via matching partial and full features".

Citation

• Accepted to ICIP 2021
• Please cite our paper if the code is helpful to your research. arxiv

@article{jo2021puzzle,
  title={Puzzle-CAM: Improved localization via matching partial and full features},
  author={Jo, Sanghyun and Yu, In-Jae},
  journal={arXiv preprint arXiv:2101.11253},
  year={2021}
}

Abstract

Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most advanced approaches are based on class activation maps (CAMs) to generate pseudo-labels to train the segmentation network. The main limitation of WSSS is that the process of generating pseudo-labels from CAMs which use an image classifier is mainly focused on the most discriminative parts of the objects. To address this issue, we propose Puzzle-CAM, a process minimizes the differences between the features from separate patches and the whole image. Our method consists of a puzzle module (PM) and two regularization terms to discover the most integrated region of in an object. Without requiring extra parameters, Puzzle-CAM can activate the overall region of an object using image-level supervision. In experiments, Puzzle-CAM outperformed previous state-of-the-art methods using the same labels for supervision on the PASCAL VOC 2012 test dataset.

Overview

Prerequisite

• Python 3.8, PyTorch 1.7.0, and more in requirements.txt
• CUDA 10.1, cuDNN 7.6.5
• 4 x Titan RTX GPUs

Usage

Install python dependencies

python3 -m pip install -r requirements.txt

Download PASCAL VOC 2012 devkit

Follow instructions in http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit

1. Train an image classifier for generating CAMs

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_classification_with_puzzle.py --architecture resnest101 --re_loss_option masking --re_loss L1_Loss --alpha_schedule 0.50 --alpha 4.00 --tag ResNeSt101@Puzzle@optimal --data_dir $your_dir

2. Apply Random Walk (RW) to refine the generated CAMs

2.1. Make affinity labels to train AffinityNet.

CUDA_VISIBLE_DEVICES=0 python3 inference_classification.py --architecture resnest101 --tag ResNeSt101@Puzzle@optimal --domain train_aug --data_dir $your_dir
python3 make_affinity_labels.py --experiment_name ResNeSt101@Puzzle@optimal@train@scale=0.5,1.0,1.5,2.0 --domain train_aug --fg_threshold 0.40 --bg_threshold 0.10 --data_dir $your_dir

2.2. Train AffinityNet.

CUDA_VISIBLE_DEVICES=0 python3 train_affinitynet.py --architecture resnest101 --tag AffinityNet@ResNeSt-101@Puzzle --label_name ResNeSt101@Puzzle@optimal@train@scale=0.5,1.0,1.5,2.0@aff_fg=0.40_bg=0.10 --data_dir $your_dir

3. Train the segmentation model using the pseudo-labels

3.1. Make segmentation labels to train segmentation model.

CUDA_VISIBLE_DEVICES=0 python3 inference_rw.py --architecture resnest101 --model_name AffinityNet@ResNeSt-101@Puzzle --cam_dir ResNeSt101@Puzzle@optimal@train@scale=0.5,1.0,1.5,2.0 --domain train_aug --data_dir $your_dir
python3 make_pseudo_labels.py --experiment_name AffinityNet@ResNeSt-101@Puzzle@train@beta=10@exp_times=8@rw --domain train_aug --threshold 0.35 --crf_iteration 1 --data_dir $your_dir

3.2. Train segmentation model.

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_segmentation.py --backbone resnest101 --mode fix --use_gn True --tag DeepLabv3+@ResNeSt-101@Fix@GN --label_name AffinityNet@ResNeSt-101@Puzzle@train@beta=10@exp_times=8@rw@crf=1 --data_dir $your_dir

4. Evaluate the models

CUDA_VISIBLE_DEVICES=0 python3 inference_segmentation.py --backbone resnest101 --mode fix --use_gn True --tag DeepLabv3+@ResNeSt-101@Fix@GN --scale 0.5,1.0,1.5,2.0 --iteration 10

python3 evaluate.py --experiment_name DeepLabv3+@ResNeSt-101@Fix@GN@val@scale=0.5,1.0,1.5,2.0@iteration=10 --domain val --data_dir $your_dir/SegmentationClass

5. Results

Qualitative segmentation results on the PASCAL VOC 2012 validation set. Top: original images. Middle: ground truth. Bottom: prediction of the segmentation model trained using the pseudo-labels from Puzzle-CAM.

Methods	background	aeroplane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	diningtable	dog	horse	motorbike	person	pottedplant	sheep	sofa	train	tvmonitor	mIoU
Puzzle-CAM with ResNeSt-101	88.9	87.1	38.7	89.2	55.8	72.8	89.8	78.9	91.3	26.8	84.4	40.3	88.9	81.9	83.1	34.0	60.1	83.6	47.3	59.6	38.8	67.7
Puzzle-CAM with ResNeSt-269	91.1	87.2	37.3	86.8	61.4	71.2	92.2	86.2	91.8	28.6	85.0	64.1	91.8	82.0	82.5	70.7	69.4	87.7	45.4	67.0	37.7	72.2

For any issues, please contact Sanghyun Jo, [email protected]